作者：王晗¹, 何刘²

作者单位：1. 中国南车股份有限公司中央研究院, 北京 100036;
2. 西南交通大学 牵引动力国家重点实验室, 四川 成都 610031

关键词：轮对轴承;最小熵解卷积;包络谱;故障诊断

摘要：

针对强噪声下轮对轴承弱故障特征难以提取,以及在实际信号检测中检测信号在故障点到检测点的传播路径中有变形和失真导致实际采集信号成分复杂难以判别的问题,提出基于最小熵解卷积的轴承故障诊断方法。该方法的核心是利用熵最小原理设计最优滤波器,突出信号中的脉冲冲击,使滤波后信号近似于原始冲击信号,消除检测中传递路径对信号的干扰,对解卷积后的信号做包络谱分析达到轮对轴承故障诊断的目的。通过实验分析,基于最小熵解卷积的轴承故障诊断方法能很好突出冲击脉冲,在包络谱中能够准确检测到故障的基频和高次谐波。

Wheel bearing fault diagnosis based on minimum entropy deconvolution method

WANG Han¹, HE Liu²

1. Central Academy of CSR Corporation Limited, Beijing 100036, China;
2. State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China

Abstract: A new approach to diagnose wheel bearing failure has been proposed with minimum entropy deconvolution(MED) to extract weak fault features of wheel bearings in strong background noise and ensure in actual signal detections that the detection signals are undistorted when passing from fault points to detection points. The core of this new approach was to design an optimal filter via MED, which was used to filter the vibration signals of wheel bearing axle boxes and make them close to the original impact signals, that is, to eliminate the interfering signals of propagation paths. The signals, after filtering, were analyzed with envelope spectrum to diagnose wheel bearing failure. Experiments have indicated that the MED method can accurately detect the fundamental frequency and harmonic components of wheel bearing faults.

Keywords: wheel bearings;MED;envelope spectrum;fault diagnosis

2016, 42(1): 114-120  收稿日期: 2015-05-16;收到修改稿日期: 2015-07-17

基金项目: 

作者简介: 王晗(1979-),男,辽宁沈阳市人,高级工程师,主要从事非线性非平稳信号处理和故障诊断工作。

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